Hewlett-Packard Development Company, L.p.

Google Inc.

Divya Sharma

Daniel R Tretter

Su Wang

Ilya Gerasimets

Bryan Christopher Horling

Kar-Han Tan

Jian Fan

Maryam Garrett

Jinman Kang

Angel L Rodriguez

Wei Chen

Yun David Tang

Kar Han Tan

Molly Murphy

Diogo Strube De Lima

Onur Ozdemir

Hung-Shuo Tai

Ron Eden

Sean Jordan

Diogo S De Lima

Dante Dibenedetto

Arsenii Saranin

Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.

Determining whether and/or how to implement request to prevent provision of search result(s)

In one example implementation, a system for aligning of images of a three-dimensional (3D) object includes a select engine to select a first image and a second image from a sequence of images of a 3D object taken at different object orientations from a capture cycle of a scanner, a combine engine to combine first metadata from the first image and second metadata from the second image to form combined metadata, an orientation engine to select a third image from the sequence of images, where the third image is from an opposite object orientation relative to a first object orientation of the first image or a second object orientation of the second image, and an align engine to align the sequence of images based on the combined metadata and third metadata from the third image.

Alignment of images of a three-dimensional object

According to one example for segmenting image data, image data comprising color pixel data, IR data, and depth data is received from a sensor. The image data is segmented into a first list of objects based on at least one computed feature of the image data. At least one object type is determined for at least one object in the first list of objects. The segmentation of the first list of objects is refined into a second list of objects based on the at least one object type. In an example, the second list of objects is output.

Image data segmentation

In one implementation, a system for using a scan recommendation includes a receiver engine to receive a plurality of pictures of a three-dimensional (3D) object from a scanner, a model engine to generate a 3D model of the 3D object by aligning the plurality of pictures of the 3D object, an analysis engine to analyze the 3D model for a volume, a shape, and a color of the 3D object, wherein the volume, the shape, and the color analysis is used to generate scan recommendations, and a display engine to display information relating to the scan recommendations based on the volume, the shape, and the color analysis of the 3D model of the 3D object.

Scan recommendations

In one implementation, a system for three-dimensional object model tagging includes a model engine to generate a model of a three-dimensional (3D) object from a pre-scan of a 3D object; an analysis engine to analyze the model to identify a property of the 3D object predictive of a reconstruction of the 3D object; a tag engine to generate a tag that describes the identified property based on the analysis; and a display engine to display the tag.

Three-dimensional object model tagging

Growing community of inventors

Palo Alto, CA, United States of America

Divya Sharma